Abnormality classification from electrocardiograms with various lead combinations

Abstract

As cardiovascular diseases have been one of the leading causes of death, early and accurate diagnosis of cardiac abnormalities with less cost becomes particularly important. Given the electrocardiogram (ECG) datasets from multiple sources, there exist many challenges to develop the generalized models that can identify multiple types of cardiac abnormalities from both 12-lead ECG signals and reduced-lead ECG signals. In this study, our objective is to build robust models which can accurately classify 30 types of abnormalities from various lead combinations of ECG signals. Given the challenges of this problem, we proposed a framework for building robust models for ECG signal classification. Firstly, a pre-processing workflow was adopted for each ECG dataset to mitigate the problem of data divergence. Secondly, to capture the lead-wise relations, we used a squeeze-and-excitation deep residual network (SE_ResNet) as our base model. Thirdly, we proposed the cross relabeling strategy and applied the sign-augmented loss function to tackle the corrupted labels in the data. Furthermore, we utilized a pos-if-any-pos ensemble strategy and a dataset-wise cross evaluation strategy to handle the uncertainty of the data distribution in the application. In the Physionet/Computing in Cardiology Challenge 2021, our approach achieved the challenge metric scores of 0.57, 0.59, 0.59, 0.58, 0.57 on 12, 6, 4, 3, 2 lead versions and an averaged challenge metric score of 0.58 over all the lead versions.Using the proposed framework, we developed the models from several large datasets with sufficiently labeled abnormalities. Our models could identify 30 ECG abnormalities accurately based on various lead combinations of ECG signals. The performance on hidden test data demonstrated the effectiveness of the proposed approaches.

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

“…Five models based on deep residual convolutional neural networks were optimized using an asymmetric loss function to classify multi-lead ECGs. More recently, lead-wise relations were captured in a study [79] using a SE deep residual network. The authors proposed a cross-relabeling strategy and applied the sign-augmented loss function to tackle the corrupted labels in the data.…”

Current and Future Use of Artificial Intelligence in Electrocardiography

“…Ribeiro et al [25] found that AI outperformed cardiology resident medical doctors in recognizing six types of abnormalities in 12-lead ECG recordings with F1 scores above 80% and specificity over 99%. There is a wide range of arrhythmias in which both AI sensitivity and specificity are higher than those achieved by state-of-the-art classifiers [26], and AI can already identify 27-30 ECG abnormalities accurately based on various lead combinations of ECG signals [27,28]. AI is more accurate than physicians working in cardiology departments at distinguishing a range of distinct arrhythmias in single-label and multi-label ECGs [27].…”

Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network

“…Lai et al proposed a deep learning model using the optimal 4-lead subset that outperformed the classification performance of the complete 12-lead ECG on normal and eight arrhythmias ( Lai et al, 2021 ). References ( Jimenez-Serrano et al, 2022 ; Xu et al, 2022 ) used deep learning-based methods to discriminate multiple cardiac conditions with various lead combinations, namely six leads (I, II, III, aVR, aVL, aVF), four leads (I, II, III, V2), three leads (I, II, V2) and two leads (I, II) vs the standard 12-lead ECG, and the data were provided during the PhysioNet/Computing in Cardiology Challenge 2021. In our previous work ( Zhang et al, 2021 ), we addressed the classification of AF and eight other types of arrhythmias utilizing RP representation of ECG signals based on the identified optimal leads (lead II and aVR) via the Inception-ResNet V2 framework in which general optimal leads were selected for nine types of arrhythmia classification.…”